Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Abstract

Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure-activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

Keywords: data curation; nano-mixture; predictive models; toxicity.

Figure 1

Number of studies on toxicity of nano-mixtures classified by year and type of nano-mixture (A), by type of toxicity test (B), and by nanomaterials and mixture components (C).

Figure 2

Number of studies on toxicity of nano-mixtures classified by group of test systems (A). Number of studies on toxicity of nano-mixtures classified by nanomaterials in nano-mixtures for crustaceans (B), fish (C), and bacteria (D). (SWCNT: single-walled carbon nanotube, MWCNT: multiple-walled carbon nanotube, NMs: nanomaterials).

Figure 3

Number of studies on toxicity of nano-mixtures classified by endpoints for D. magna (A), D. rerio (B), and E. coli (C).